Anne Arundel Medical Center, Department of Orthopedics, Annapolis, MD.
J Arthroplasty. 2020 Aug;35(8):2109-2113.e1. doi: 10.1016/j.arth.2020.03.038. Epub 2020 Mar 27.
The Centers for Medicare and Medicaid Services has removed total hip arthroplasty from the inpatient-only (IO) list in January 2020. Given the confusion created when total knee arthroplasty came off the IO list in 2018, this study aims to develop a predictive model for guiding preoperative inpatient admission decisions based upon readily available patient demographic and comorbidity data.
This is a retrospective review of 1415 patients undergoing elective unilateral primary THA between January 2018 and October 2019. Multiple logistic regression was used to develop a model for predicting LOS ≥2 days based on preoperative demographics and comorbidities.
Controlling for other demographics and comorbidities, increased age (odds ratio [OR], 1.048; P < .001), female gender (OR, 2.284; P < .001), chronic obstructive pulmonary disorder (OR, 2.249; P = .003), congestive heart failure (OR, 8.231; P < .001), and number of comorbidities (OR, 1.216; P < .001) were associated with LOS ≥2 days while patients with increased body mass index (OR, 0.964; P = .007) and primary hypertension (OR, 0.671; P = .008) demonstrated significantly reduced odds of staying in the hospital for 2 or more days. The area under the curve was found to be 0.731, indicating acceptable discriminatory value.
For patients undergoing primary THA, increased age, female gender, chronic obstructive pulmonary disorder, congestive heart failure, and multiple comorbidities are risk factors for inpatient hospital LOS of 2 or more days. Our predictive model based on readily available patient presentation and comorbidity characteristics may aid surgeons in preoperatively identifying patients requiring inpatient admission with removal of THA from the Medicare IO list.
2020 年 1 月,医疗保险和医疗补助服务中心将全髋关节置换术从仅限住院治疗(IO)名单中移除。鉴于 2018 年全膝关节置换术从 IO 名单中移除时造成的混乱,本研究旨在开发一种预测模型,以便根据患者的人口统计学和合并症数据,指导术前住院决策。
这是一项对 2018 年 1 月至 2019 年 10 月期间接受择期单侧初次全髋关节置换术的 1415 名患者的回顾性研究。使用多变量逻辑回归分析,根据术前人口统计学和合并症数据,建立预测 LOS≥2 天的模型。
控制其他人口统计学和合并症后,年龄增加(比值比[OR],1.048;P<0.001)、女性(OR,2.284;P<0.001)、慢性阻塞性肺疾病(OR,2.249;P=0.003)、充血性心力衰竭(OR,8.231;P<0.001)和合并症数量(OR,1.216;P<0.001)与 LOS≥2 天相关,而 BMI 增加(OR,0.964;P=0.007)和原发性高血压(OR,0.671;P=0.008)的患者住院 2 天以上的可能性显著降低。曲线下面积为 0.731,表明具有可接受的鉴别价值。
对于接受初次全髋关节置换术的患者,年龄增加、女性、慢性阻塞性肺疾病、充血性心力衰竭和多种合并症是住院 2 天或以上的危险因素。我们基于患者就诊时的表现和合并症特征建立的预测模型,可能有助于外科医生在全髋关节置换术不再属于医疗保险 IO 名单的情况下,术前识别需要住院的患者。